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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRSDate not supplied

Holistic and Pragmatic Standards Processes Enable Interdisciplinary Science

Open, interdisciplinary science inevitably relies heavily on standards. Standards are those often unseen agreements that we take for granted when systems and processes are working fine. Yet standards work is perpetual, laborious, and sometimes contentious, especially for standards to work across diverse disciplines. Standards development, maintenance, and implementation is a complex, ongoing socio-technical process. NASA has developed a progressively open science policy and strategy that calls for the establishment of a data standards process reaching across the five diverse divisions of the Science Mission Directorate. This is a delicate exercise. We, therefore, seek to apply a holistic yet pragmatic approach to developing and maintaining a standards process. We adopt an ecological philosophy that focuses on the interactions within the data ecosystem and how standards facilitate those interactions. We couple high-level analysis with on the ground experimentation. We began by 1) mapping information ecosystem components (e.g. data centers, missions, services, protocols, users), 2)establishing how the components interact (e.g. sharing (meta)data, funding, personnel exchange), and 3) modelling system dynamics (e.g. creation of products from multiple data centers, redundant processes, shared services). The goal is to apply understanding of the ecosystem to real world applications (e.g. planning a new mission, implementing new policy requirements, improving process efficiency, etc.). We have also conducted studies of historical standardization efforts, documenting lessons learned and cautionary tales. We then contrast this more abstract work with real examples. We reviewed and assessed multiple existing standards development processes both within and external to NASA. We now work to implement an initial test process which can be further optimized. We seek to define a consistent approach for assigning persistent identifiers for research objects, especially for the purposes of citation. The experience from this relatively ‘simple’ test case adds a pragmatic perspective on how researchers and engineers actually work. This presentation will review the details of this methodology, our initial findings, and how they might apply to other interdisciplinary standardization efforts.

Mark A Parsons↗
NASA NTRS2022

Mars Terrain Segmentation with Less Labels

Planetary rover systems need to perform terrain segmentation to identify drivable areas as well as identify specific types of soil for sample collection. The latest Martian terrain segmentation methods rely on supervised learning which is very data hungry and difficult to train where only a small number of labeled samples are available. Moreover, the semantic classes are defined differently for different applications (e.g., rover traversal vs. geological) and as a result the network has to be trained from scratch each time, which is an inefficient use of resources. This research proposes a semi-supervised learning framework for Mars terrain segmentation where a deep segmentation network trained in an unsupervised manner on unlabeled images is transferred to the task of terrain segmentation trained on few labeled images. The network incorporates a backbone module which is trained using a contrastive loss function and an output atrous convolution module which is trained using a pixel-wise cross-entropy loss function. Evaluation results using the metric of segmentation accuracy show that the proposed method with contrastive pre-training outperforms plain supervised learning by 2%-10%. Moreover, the proposed model is able to achieve a segmentation accuracy of 91.1% using only 161 training images (1% of the original dataset) compared to 81.9% with plain supervised learning.

Wilson, Brian D↗
NASA NTRS1992

Closed ecological systems: From test tubes to Earth's biosphere

Artificially constructed closed ecological systems (CES) have been researched both experimentally and theoretically for over 25 years. The size of these systems have varied from less than one liter to many thousands of cubic meters in volume. The diversity of the included components has a similarly wide range from purely aquatic systems to soil based systems that incorporate many aspects of Earth's biosphere. While much has been learned about the functioning of these closed systems, much remains to be learned. In this paper, we compare and contrast the behavior of closed ecological systems of widely different sizes through an analysis of their atmospheric composition. In addition, we will compare the performance of relatively small CES with the behavior of Earth's biosphere. We address the applicability of small CES as replicable analogs for planetary biospheres and discuss the use of small CES as an experimental milieu for an examination of the evolution of extra-terrestrial colonies.

Frye, Robert J.↗
NASA NTRS1992

Martian sedimentary deposits

The objectives are characterization of flow through outflow channels, sedimentation associated with Martian outflow systems, and documentation of Martian lakes. Over the period of the grant much, but not all, of the study centered on the Maja Valles outflow. Maja served as an example in which the effects of multiple channel routing and ponding could be studied. Maja Valles also served as the test case for calculating flow through an outflow system. Applying the lessons learned in Maja Valles and comparisons and contrast required a scrutiny of other channels.

Dehon, Rene↗
NASA NTRS2003

Integration, Testing and Performance of the Infrared Multi-Object Spectrometer

The Infrared Multi-Object Spectrometer (IRMOS) is a principle investigator-class instrument for the Kitt Peak National Observatory 2.1 m and Mayall 3.8 m telescopes. IRMOS is a near-IR (0.8 - 2.5 micron) spectrometer with low-to mid-resolving power (R = lambda/delta lambda = 300 - 3000). On the 3.8 m telescope, IRMOS produces simultaneous spectra of approximately 100 objects in its approximately 3 x 2 arcmin field of view using a commercial micro electro-mechanical systems (MEMS) digital micro-mirror device (DMD) from Texas Instruments. The multi-mirror array DMD operates as a real-time programmable slit mask. The all-reflective optical design consists of two imaging subsystems. The focal reducer images the focal plane of the telescope onto the DMD field stop, and the spectrograph images the DMD onto a large-format detector. The instrument operates at approximately 80 K, cooled by a single electro-mechanical cryocooler. The bench and all components are made from aluminum 6061-T651. There are three cryogenic mechanisms. We describe laboratory integration and test of IRMOS before shipment to Kitt Peak. We give an overview of the optical alignment technique and integration of optical, mechanical, electrical and cryogenic subsystems. We compare optical test results to model predictions of point spread function size and morphology, contrast, and stray light. We discuss some lessons learned and conclude with a prediction for performance on the telescope.

Ohl, Raymond G.↗
NASA NTRS2015

ACCEPT: Introduction of the Adverse Condition and Critical Event Prediction Toolbox

The prediction of anomalies or adverse events is a challenging task, and there are a variety of methods which can be used to address the problem. In this paper, we introduce a generic framework developed in MATLAB (sup registered mark) called ACCEPT (Adverse Condition and Critical Event Prediction Toolbox). ACCEPT is an architectural framework designed to compare and contrast the performance of a variety of machine learning and early warning algorithms, and tests the capability of these algorithms to robustly predict the onset of adverse events in any time-series data generating systems or processes.

machine learning↗
NASA NTRS2021

AI4MARS: A Dataset for Terrain-Aware Autonomy on Mars

Deep learning has quickly become a necessity for selfdriving vehicles on Earth. In contrast, the self-driving vehicles on Mars, including NASA’s latest rover, Perseverance, which is planned to land on Mars in February 2021, are still driven by classical machine vision systems. Deep learning capabilities, such as semantic segmentation and object recognition, would substantially benefit the safety and productivity of ongoing and future missions to the red planet. To this end, we created the first large-scale dataset, AI4Mars, for training and validating terrain classification models for Mars, consisting of ~326K semantic segmentation full image labels on 35K images from Curiosity, Opportunity, and Spirit rovers, collected through crowdsourcing. Each image was labeled by ~10 people to ensure greater quality and agreement of the crowdsourced labels. It also includes ~1.5K validation labels annotated by the rover planners and scientists from NASA’s MSL (Mars Science Laboratory) mission, which operates the Curiosity rover, and MER (Mars Exploration Rovers) mission, which operated the Spirit and Opportunity rovers. We trained a DeepLabv3 model on the AI4Mars training dataset and achieved over 96% overall classification accuracy on the test set. The dataset is made publicly available.1

Ono, Hiro↗
NASA NTRS1989

The Marr and Albus Theories of the Cerebellum: Two Eary Models of Associative Memory

The Marr and Albus theories of the cerebellum are compared and contrasted. They are shown to be similar in their analysis of the function of the mossy fibers, granule cells, Golgi cells, and Purkinje cells. They both predict motor learning in the parallel fiber synapses on the Purkinje dendrites mediated by concurrent climbing fiber input. This prediction has been confirmed by experimental evidence. In contrast, Marr predicts these synapses would be facilitated by learning, while Albus predicts they would be weakened. Experimental evidence confirms synaptic weakening.

Albus, James S.↗
NASA NTRS2021

Exploring Sentinel-1 and Sentinel-2 diversity for Flood inundation mapping using deep learning

Identification of flood water extent from satellite images has historically relied on either synthetic aperture radar (SAR) or multi-spectral (MS) imagery. MS sensors are limited to cloud free conditions, whereas SAR imagery is plagued by noise-like speckle. Prior studies that use combinations of MS and SAR data to overcome individual limitations of these sensors have not fully examined sensitivity of flood mapping performance to different combinations of SAR and MS derived spectral indices or band transformations in color space. This study explores the use of diverse bands of Sentinel 2 (S2) through well-established water indices and Sentinel 1 (S1) derived SAR imagery along with their combinations to assess their capability for generating accurate flood inundation maps. The robustness in performance of S-1 and S-2 band combinations was evaluated using 446 hand labeled flood inundation images spanning across 11 flood events from Sen1Floods11 dataset which are highly diverse in terms of land cover as well as location. A modified K-fold cross validation approach is used to evaluate the performance of 32 combinations of S1 and S2 bands using a fully connected deep convolutional neural network known as U-Net. Our results indicated that usage of elevation information has improved the capability of S1 imagery to produce more accurate flood inundation maps. Compared to a median F1 score of 0.62 when using only S1 bands, the combined use of S1 and elevation information led to an improved median F1 score of 0.73. Water extraction indices based on S2 bands have a statistically significant superior performance in comparison to S1. Among all the band combinations, HSV (Hue, Saturation, Value) transformation of S2 bands provides a median F1 score of 0.9, outperforming the commonly used water spectral indices owing to HSV’s transformation’s superior contrast distinguishing abilities. Additionally, U-Net algorithm was able to learn the relationship between raw S2 based water extraction indices and their corresponding raw S2 bands, but not of HSV owing to relatively complex computation involved in the latter. Results of the paper establishes important benchmarks for the extension of S1 and S2 data-based flood inundation mapping efforts over large spatial extents.

Goutam Konapala↗
NASA NTRS2016

A Quantum-Assisted Algorithm for Sampling Applications in Machine Learning

An increase in the efficiency of sampling from Boltzmann distributions would have a significant impact in deep learning and other machine learning applications. Recently, quantum annealers have been proposed as a potential candidate to speed up this task, but several limitations still bar these state-of-the-art technologies from being used effectively. One of the main limitations is that, while the device may indeed sample from a Boltzmann-like distribution, quantum dynamical arguments suggests it will do so with an instance-dependent effective temperature, different from the physical temperature of the device. Unless this unknown temperature can be unveiled, it might not be possible to effectively use a quantum annealer for Boltzmann sampling. In this talk, we present a strategy to overcome this challenge with a simple effective-temperature estimation algorithm. We provide a systematic study assessing the impact of the effective temperatures in the learning of a kind of restricted Boltzmann machine embedded on quantum hardware, which can serve as a building block for deep learning architectures. We also provide a comparison to k-step contrastive divergence (CD-k) with k up to 100. Although assuming a suitable fixed effective temperature also allows to outperform one step contrastive divergence (CD-1), only when using an instance-dependent effective temperature we find a performance close to that of CD-100 for the case studied here. We discuss generalizations of the algorithm to other more expressive generative models, beyond restricted Boltzmann machines.

Perdomo-Ortiz, Alejandro↗
NASA NTRS1998

Effects of sleep loss, time of day, and extended mental work on implicit and explicit learning of sequences

Tacit knowledge is part of many professional skills and can be studied experimentally with implicit-learning paradigms. The authors explored the effects of 2 different stressors, loss of sleep and mental fatigue, on implicit learning in a serial-response time (RT) task. In the 1st experiment, 1 night of sleep deprivation was shown to impair implicit but not explicit sequence learning. In the 2nd experiment, no impairment of both types of sequence learning was found after 1.5 hr of mental work. Serial-RT performance, in contrast, suffered from both stressors. These findings suggest that sleep deprivation induces specific risks for automatic, skill-based behavior that are not present in consciously controlled performance.

Learning↗
NASA NTRSDate not supplied

Advancements in Blowing Dust Detection at Night via Machine Learning

This presentation introduces operational users to a machine-learning based Dust Probability product developed by the NASA SPoRT program for the application of detecting and monitoring blowing dust plumes at night. Advances in earth observing satellites has improved monitoring and detection of dust both day and night through derived imagery such as the Dust RGB. However, limitations of the RGB at night result in less contrast between dust and land surface features, as seen by the user. A Machine Learning (ML) model has been developed and applied to GOES-16 ABI to overcome this limitation and improve nighttime dust detection. The ML capability is a subset of Artificial Intelligence methods. In this case the Dust ML model was developed using a simple Random Forest (RF) model, typically used to solve classification challenges (or to provide regression type output). The goal was to leverage the strengths of the RF model to learn how to identify blowing dust, and hence, overcome the limitation of a user trying to detect blowing dust within the satellite imagery by eye alone. A brief description of the ML model development will be provided. However, the focus of the presentation will be on the initial user feedback from the assessment of this tool for the 2022 blowing dust events of March through April. During this time several users across the U.S. Southwest collaborated to apply this Dust ML product at night as a complement to the existing Dust RGB in order to determine if it provided greater operational efficiency and value.

Machine Learning↗
NASA NTRS2017

Lessons Learned from Applying Design Thinking in a NASA Rapid Design Study in Aeronautics

In late 2015, NASA's Aeronautics Research Mission Directorate (ARMD) funded an experiment in rapid design and rapid teaming to explore new approaches to solving challenging design problems in aeronautics in an effort to cultivate and foster innovation. This report summarizes several lessons learned from the rapid design portion of the study. This effort entailed learning and applying design thinking, a human-centered design approach, to complete the conceptual design for an open-ended design challenge within six months. The design challenge focused on creating a capability to advance experimental testing of autonomous aeronautics systems, an area of great interest to NASA, the US government as a whole, and an entire ecosystem of users and developers around the globe. A team of nine civil servant researchers from three of NASA's aeronautics field centers with backgrounds in several disciplines was assembled and rapidly trained in design thinking under the guidance of the innovation and design firm IDEO. The design thinking process, while used extensively outside the aerospace industry, is less common and even counter to many practices within the aerospace industry. In this report, several contrasts between common aerospace research and development practices and design thinking are discussed, drawing upon the lessons learned from the NASA rapid design study. The lessons discussed included working towards a design solution without a set of detailed design requirements, which may not be practical or even feasible for management to ascertain for complex, challenging problems. This approach allowed for the possibility of redesigning the original problem statement to better meet the needs of the users. Another lesson learned was to approach problems holistically from the perspective of the needs of individuals that may be affected by advances in topic area instead of purely from a technological feasibility viewpoint. The interdisciplinary nature of the design team also provided valuable experience by allowing team members from different technological backgrounds to work side-by-side instead of dividing into smaller teams, as is frequently done in traditional multidisciplinary design. The team also learned how to work with qualitative data obtained primarily through the 70-plus interviews that were conducted over the course of this project, which was a sharp contrast to using quantitative data with regards to identifying, capturing, analyzing, storing, and recalling the data. When identifying potential interviewees who may have useful contributions to the design subject area, the team found great value in talking to non-traditional users and potential beneficiaries of autonomous aeronautics systems whose impact on the aeronautics autonomy ecosystem is growing swiftly. Finally, the team benefitted from using "sacrificial prototyping," which is a method of rapidly prototyping draft concepts and ideas with the intent of enabling potential users to provide significant feedback early in the design process. This contrasts the more common approach of using expensive prototypes that focus on demonstrating technical feasibility. The unique design approach and lessons learned by the team throughout this process culminated in a final design concept that was quite different than what the team originally assumed would be the design concept initially. A summary of the more usercentered final design concept is also provided.

McGowan, Anna-Maria↗
NASA NTRS2016

Laboratory Testing and Performance Verification of the CHARIS Integral Field Spectrograph

The Coronagraphic High Angular Resolution Imaging Spectrograph (CHARIS) is an integral field spectrograph (IFS) that has been built for the Subaru telescope. CHARIS has two imaging modes; the high-resolution mode is R82, R69, and R82 in J, H, and K bands respectively while the low-resolution discovery mode uses a second low-resolution prism with R19 spanning 1.15-2.37 microns (J+H+K bands). The discovery mode is meant to augment the low inner working angle of the Subaru Coronagraphic Extreme Adaptive Optics (SCExAO) adaptive optics system, which feeds CHARIS a coronagraphic image. The goal is to detect and characterize brown dwarfs and hot Jovian planets down to contrasts five orders of magnitude dimmer than their parent star at an inner working angle as low as 80 milliarcseconds. CHARIS constrains spectral crosstalk through several key aspects of the optical design. Additionally, the repeatability of alignment of certain optical components is critical to the calibrations required for the data pipeline. Specifically the relative alignment of the lens let array, prism, and detector must be highly stable and repeatable between imaging modes. We report on the measured repeatability and stability of these mechanisms, measurements of spectral crosstalk in the instrument, and the propagation of these errors through the data pipeline. Another key design feature of CHARIS is the prism, which pairs Barium Fluoride with Ohara L-BBH2 high index glass. The dispersion of the prism is significantly more uniform than other glass choices, and the CHARIS prisms represent the first NIR astronomical instrument that uses L-BBH2as the high index material. This material choice was key to the utility of the discovery mode, so significant efforts were put into cryogenic characterization of the material. The final performance of the prism assemblies in their operating environment is described in detail. The spectrograph is going through final alignment, cryogenic cycling, and is being delivered to the Subaru telescope in April 2016. This paper is a report on the laboratory performance of the spectrograph, and its current status in the commissioning process so that observers will better understand the instrument capabilities. We will also discuss the lessons learned during the testing process and their impact on future high-contrast imaging spectrographs for wavefront control.

Coronagraphic High Angular Resolution Imaging Spec↗
NASA NTRS2012

Hybrid Collaborative Learning for Classification and Clustering in Sensor Networks

Traditionally, nodes in a sensor network simply collect data and then pass it on to a centralized node that archives, distributes, and possibly analyzes the data. However, analysis at the individual nodes could enable faster detection of anomalies or other interesting events as well as faster responses, such as sending out alerts or increasing the data collection rate. There is an additional opportunity for increased performance if learners at individual nodes can communicate with their neighbors. In previous work, methods were developed by which classification algorithms deployed at sensor nodes can communicate information about event labels to each other, building on prior work with co-training, self-training, and active learning. The idea of collaborative learning was extended to function for clustering algorithms as well, similar to ideas from penta-training and consensus clustering. However, collaboration between these learner types had not been explored. A new protocol was developed by which classifiers and clusterers can share key information about their observations and conclusions as they learn. This is an active collaboration in which learners of either type can query their neighbors for information that they then use to re-train or re-learn the concept they are studying. The protocol also supports broadcasts from the classifiers and clusterers to the rest of the network to announce new discoveries. Classifiers observe an event and assign it a label (type). Clusterers instead group observations into clusters without assigning them a label, and they collaborate in terms of pairwise constraints between two events [same-cluster (mustlink) or different-cluster (cannot-link)]. Fundamentally, these two learner types speak different languages. To bridge this gap, the new communication protocol provides four types of exchanges: hybrid queries for information, hybrid "broadcasts" of learned information, each specified for classifiers-to-clusterers, and clusterers-to-classifiers. The new capability has the potential to greatly expand the in situ analysis abilities of sensor networks. Classifiers seeking to categorize incoming data into different types of events can operate in tandem with clusterers that are sensitive to the occurrence of new kinds of events not known to the classifiers. In contrast to current approaches that treat these operations as independent components, a hybrid collaborative learning system can enable them to learn from each other.

Wagstaff, Kiri L.↗
NASA NTRS2002

Dar Al Gani 872: Yet Another Eucrite, Yet Another Lesson to Learn?

We present chemical and mineralogical data on a new monomict basaltic eucrite recovered from Libya. In contrast to most other eucrites, it exhibits high shock features, unusually heterogeneous exsolution of pigeonite, and interesting melt pockets. Additional information is contained in the original extended abstract.

Patzer, A.↗
NASA NTRS1998

35 GHz Measurements of CO2 Crystals for Simulating Observations of the Martian Polar Caps

In order to learn more about the Martian polar caps, it is important to compare and contrast the behavior of both frozen H2O and CO2 in different parts of the electromagnetic spectrum. Relatively little attention has been given, thus far, to observing the thermal microwave part of the spectrum. In this experiment, passive microwave radiation emanating from within a 33 cm snowpack was measured with a 35 GHz hand-held radiometer, and in addition to the natural snow measurements, the radiometer was used to measure the microwave emission and scattering from layers of manufactured CO2 (dry ice). A 1 m x 2 m plate of aluminum sheet metal was positioned beneath the natural snow so that microwave emissions from the underlying soil layers would be minimized. Compared to the natural snow crystals, results for the dry ice layers exhibit lower' microwave brightness temperatures for similar thicknesses, regardless of the incidence angle of the radiometer. For example, at 50 degree H (horizontal polarization) and with a covering of 21 cm of snow and 18 cm of dry ice, the brightness temperatures were 150 K and 76 K, respectively. When the snow depth was 33 cm, the brightness temperature was 144 K, and when the total thickness of the dry ice was 27 cm, the brightness temperature was 86 K. The lower brightness temperatures are due to a combination of the lower physical temperature and the larger crystal sizes of the commercial CO2 Crystals compared to the snow crystals. As the crystal size approaches the size of the microwave wavelength, it scatters microwave radiation more effectively, thus lowering the brightness temperature. The dry ice crystals in this experiment were about an order of magnitude larger than the snow crystals and three orders of magnitude larger than the CO2 Crystals produced in the cold stage of a scanning electron microscope. Spreading soil, approximately 2 mm in thickness, on the dry ice appeared to have no effect on the brightness temperatures.

Foster, J. L.↗
NASA NTRS2016

Mineralogy of Eolian Sands at Gale Crater

The Mars Science Laboratory rover Curiosity has been exploring outcrop and regolith in Gale crater since August 6, 2012. During this exploration, the mission has collected 10 samples for mineralogical analysis by X-ray diffraction (XRD), using the CheMin instrument. The CheMin (Chemistry and Mineralogy) instrument on the Mars Science Laboratory rover Curiosity uses a CCD detector and a Co-anode tube source to acquire both mineralogy (from the pat-tern of Co diffraction) and chemical information (from energies of fluoresced X-rays). A detailed description of CheMin is provided in [1]. As part of the rover checkout after landing, the first sample selected for analysis was an eolian sand deposit (the Rocknest "sand shadow"). This sample was selected in part to characterize unconsolidated eolian regolith, but primarily to prove performance of the scoop collection system on the rover. The focus of the mission after Rocknest was on the consolidated sediments of Gale crater, so all of the nine subsequent samples were collected by drilling into bedrock com-posed of lithified sedimentary materials, including mudstone and sandstone. No scoop samples have been collected since Rocknest, but at the time this abstract was written the mission stands poised to use the scoop again, to collect active dune sands from the Bagnold dune field. Several abstracts at this conference outline the Bagnold dune campaign and summarize preliminary results from analyses on approach to the Namib dune sampling site. In this abstract we review the mineralogy of Rocknest, contrast that with the mineralogy of local sediments, and anticipate what will be learned by XRD analysis of Bagnold dune sands.

Achilles, C. N.↗